Strategy for efficiently utilizing a heat-pump based hvac system with an auxiliary heating system

Abstract

A method, system and computer program product for efficiently utilizing a heat-pump based HVAC system with an auxiliary heating system. Possible actions (e.g., cooling, off, heat-pump heating and auxiliary heating) are selected over a period of time (e.g., three days). The effects of selecting actions are recorded in terms of a data set of tuples. A regression is fitted to model a transition function separately for each of the possible actions using the data set of tuples. A model is selected to fit a regression using regression features (e.g., historic indoor temperatures). An action (e.g., off) to take is determined using a lookahead planning approach during a don't care period (period of time occupants do not care about the inside temperature) for every time-step within the don't care period until an end of the don't care period, where the effects of the actions continue to be recorded.

1 . A method for efficiently utilizing an HVAC system, the method comprising:
 selecting each of a plurality of possible actions over a first period of time;   recording effects of selecting actions in terms of a data set of tuples during said first period of time;   selecting a model to fit a regression using regression features during a second period of time, wherein said regression features comprise a current indoor temperature, a current outdoor temperature and a plurality of historic indoor temperatures;   fitting said regression to model a transition function for each of said plurality of possible actions using said data set of tuples during said second period of time;   determining, by a processor, an action to take using a lookahead planning approach of said selected model during said second period of time for every time-step within each sub-period of said second period of time until an end of said sub-period of said second period of time, wherein said time-step corresponds to a fixed segment of time within said second period of time, wherein said action corresponds to implementing one of said plurality of possible actions; and   recording effects of selecting actions in terms of said data set of tuples during said second period of time.   
     
     
         2 . The method as recited in  claim 1 , wherein said sub-period of said second period of time occurs during a time an occupant of a residence, office or building does not care about a temperature inside said residence, office or building. 
     
     
         3 . The method as recited in  claim 1 , wherein said first period of time corresponds to an exploratory period, wherein said second period occurs after an end of said exploratory period. 
     
     
         4 . The method as recited in  claim 1 , wherein said first period of time comprises a period of time less than a week. 
     
     
         5 . The method as recited in  claim 1 , wherein a temperature during said second period of time does not exceed 100 degrees Fahrenheit and is not less than 40 degrees Fahrenheit. 
     
     
         6 . The method as recited in  claim 1 , wherein said plurality of possible actions comprises cooling, off, heat-pump heating and auxiliary heating. 
     
     
         7 . The method as recited in  claim 1 , wherein said current outdoor temperature is predicted using a weather forecast. 
     
     
         8 . The method as recited in  claim 1 , wherein said regression features further comprise energy consumed by an action previously taken. 
     
     
         9 . The method as recited in  claim 1 , wherein said data set of tuples comprises a data set of actions, states and transition states. 
     
     
         10 . A computer program product for efficiently utilizing an HVAC system, the computer program product comprising a computer readable storage medium having program code embodied therewith, the program code comprising the programming instructions for:
 selecting each of a plurality of possible actions over a first period of time;   recording effects of selecting actions in terms of a data set of tuples during said first period of time;   selecting a model to fit a regression using regression features during a second period of time, wherein said regression features comprise a current indoor temperature, a current outdoor temperature and a plurality of historic indoor temperatures;   fitting said regression to model a transition function for each of said plurality of possible actions using said data set of tuples during said second period of time;   determining an action to take using a lookahead planning approach of said selected model during said second period of time for every time-step within each sub-period of said second period of time until an end of said sub-period of said second period of time, wherein said time-step corresponds to a fixed segment of time within said second period of time, wherein said action corresponds to implementing one of said plurality of possible actions; and   recording effects of selecting actions in terms of said data set of tuples during said second period of time.   
     
     
         11 . The computer program product as recited in  claim 10 , wherein said sub-period of said second period of time occurs during a time an occupant of a residence, office or building does not care about a temperature inside said residence, office or building. 
     
     
         12 . The computer program product as recited in  claim 10 , wherein said first period of time corresponds to an exploratory period, wherein said second period occurs after an end of said exploratory period. 
     
     
         13 . The computer program product as recited in  claim 10 , wherein said first period of time comprises a period of time less than a week. 
     
     
         14 . The computer program product as recited in  claim 10 , wherein a temperature during said second period of time does not exceed 100 degrees Fahrenheit and is not less than 40 degrees Fahrenheit. 
     
     
         15 . The computer program product as recited in  claim 10 , wherein said plurality of possible actions comprises cooling, off, heat-pump heating and auxiliary heating. 
     
     
         16 . The computer program product as recited in  claim 10 , wherein said current outdoor temperature is predicted using a weather forecast. 
     
     
         17 . The computer program product as recited in  claim 10 , wherein said regression features further comprise energy consumed by an action previously taken. 
     
     
         18 . The computer program product as recited in  claim 10 , wherein said data set of tuples comprises a data set of actions, states and transition states. 
     
     
         19 . A heat-pump based HVAC system, comprising:
 a heat-pump for providing heat energy from a source of heat to a destination;   an auxiliary heating system for heating a residence, office or building when it is not energy effective to utilize said heat-pump; and   a control unit connected to said heat-pump and said auxiliary heating system, wherein said control unit comprises:
 a memory unit for storing a computer program for controlling a utilization of said heat-pump and said auxiliary heating system; and 
 a processor coupled to the memory unit, wherein the processor is configured to execute the program instructions of the computer program comprising:
 selecting each of a plurality of possible actions over a first period of time; 
 recording effects of selecting actions in terms of a data set of tuples during said first period of time; 
 selecting a model to fit a regression using regression features during a second period of time, wherein said regression features comprise a current indoor temperature, a current outdoor temperature and a plurality of historic indoor temperatures; 
 fitting said regression to model a transition function for each of said plurality of possible actions using said data set of tuples during said second period of time; 
 determining an action to take using a lookahead planning approach of said selected model during said second period of time for every time-step within each sub-period of said second period of time until an end of said sub-period of said second period of time, wherein said time-step corresponds to a fixed segment of time within said second period of time, wherein said action corresponds to implementing one of said plurality of possible actions; and 
 recording effects of selecting actions in terms of said data set of tuples during said second period of time. 
 
   
     
     
         20 . The heat-pump based HVAC system as recited in  claim 19 , wherein said sub-period of said second period of time occurs during a time an occupant of a residence, office or building does not care about a temperature inside said residence, office or building. 
     
     
         21 . The heat-pump based HVAC system as recited in  claim 19 , wherein said first period of time corresponds to an exploratory period, wherein said second period occurs after an end of said exploratory period. 
     
     
         22 . The heat-pump based HVAC system as recited in  claim 19 , wherein said first period of time comprises a period of time less than a week. 
     
     
         23 . The heat-pump based HVAC system as recited in  claim 19 , wherein a temperature during said second period of time does not exceed 100 degrees Fahrenheit and is not less than 40 degrees Fahrenheit. 
     
     
         24 . The heat-pump based HVAC system as recited in  claim 19 , wherein said plurality of possible actions comprises cooling, off, heat-pump heating and auxiliary heating. 
     
     
         25 . The heat-pump based HVAC system as recited in  claim 19 , wherein said current outdoor temperature is predicted using a weather forecast. 
     
     
         26 . The heat-pump based HVAC system as recited in  claim 19 , wherein said regression features further comprise energy consumed by an action previously taken. 
     
     
         27 . The heat-pump based HVAC system as recited in  claim 19 , wherein said data set of tuples comprises a data set of actions, states and transition states. 
     
     
         28 . The heat-pump based HVAC system as recited in  claim 19 , wherein said auxiliary heating system comprises a resistive heat coil.